Discover and search across all your AWS data sets

The AWS Glue Data Catalog is your persistent metadata store for all your data assets, regardless of where they are located. The Data Catalog contains table definitions, job definitions, schemas, and other control information to help you manage your AWS Glue environment. It automatically computes statistics and registers partitions to make queries against your data efficient and cost-effective. It also maintains a comprehensive schema version history so you can understand how your data has changed over time.

Automatic schema discovery

AWS Glue crawlers connect to your source or target data store, progresses through a prioritized list of classifiers to determine the schema for your data, and then creates metadata in your AWS Glue Data Catalog. The metadata is stored in tables in your data catalog and used in the authoring process of your ETL jobs. You can run crawlers on a schedule, on-demand, or trigger them based on an event to ensure that your metadata is up-to-date.

Manage and enforce schemas for data streams

AWS Glue Schema Registry, a serverless feature of AWS Glue, enables you to validate and control the evolution of streaming data using registered Apache Avro schemas, at no additional charge. Through Apache-licensed serializers and deserializers, the Schema Registry integrates with Java applications developed for Apache Kafka, Amazon Managed Streaming for Apache Kafka (MSK), Amazon Kinesis Data Streams, Apache Flink, Amazon Kinesis Data Analytics for Apache Flink, and AWS Lambda. When data streaming applications are integrated with the Schema Registry, you can improve data quality and safeguard against unexpected changes using compatibility checks that govern schema evolution. Additionally, you can create or update AWS Glue tables and partitions using schemas stored within the registry.

Automatically scale based on workload

Autoscaling in AWS Glue, a serverless feature in AWS Glue, dynamically scales resources up and down based on workload. With Autoscaling, your job is assigned workers only when needed. As the job progresses, and it goes through advanced transforms, AWS Glue adds and removes resources depending on how much it can split up the workload. You no longer need to worry about over-provisioning resources, spend time optimizing the number of workers, or pay for idle resources.


Deduplicate and cleanse data with built-in machine learning

AWS Glue helps clean and prepare your data for analysis without becoming a machine learning expert. Its FindMatches feature deduplicates and finds records that are imperfect matches of each other. For example, use FindMatches to find duplicate records in your database of restaurants, such as when one record lists “Joe's Pizza” at “121 Main St.” and another shows a “Joseph's Pizzeria” at “121 Main”. FindMatches will just ask you to label sets of records as either “matching” or “not matching.” The system will then learn your criteria for calling a pair of records a “match” and will build an ETL job that you can use to find duplicate records within a database or matching records across two databases.

Edit, debug, and test ETL code with developer endpoints

If you choose to interactively develop your ETL code, AWS Glue provides development endpoints for you to edit, debug, and test the code it generates for you. You can use your favorite IDE or notebook. You can write custom readers, writers, or transformations and import them into your AWS Glue ETL jobs as custom libraries. You can also use and share code with other developers in our GitHub repository.

Normalize data without code using a visual interface

AWS Glue DataBrew provides an interactive, point-and-click visual interface for users like data analysts and data scientists to clean and normalize data without writing code. You can easily visualize, clean, and normalize data directly from your data lake, data warehouses, and databases, including Amazon S3, Amazon Redshift, Amazon Aurora, and Amazon RDS. You can choose from over 250 built-in transformations to combine, pivot, and transpose the data, and automate data preparation tasks by applying saved transformations directly to the new incoming data.

Define, detect, and remediate sensitive data

AWS Glue Sensitive Data Detection lets you define, identify, and process sensitive data in your data pipeline and in your data lake. Once identified, you can remediate sensitive data by redacting, replacing, or reporting on personally identifiable information (PII) data and other types of data deemed sensitive. AWS Glue Sensitive Data Detection simplifies the identification and masking of sensitive data, including PII such as name, SSN, address, email, and driver’s license.

Scale existing Python code with Ray (Preview)

Developers like Python for its ease of use and rich collection of built-in data processing libraries. They want to use familiar Python primitive type for processing large datasets. AWS Glue for Ray helps data engineers process large datasets using Python and popular Python libraries. AWS Glue for Ray uses, an open-source unified compute framework that helps scale Python workloads from a single node to hundreds of nodes. AWS Glue for Ray is serverless, so there is no infrastructure to manage.

Create custom visual transforms

AWS Glue helps you create custom visual transforms so you can define, reuse, and share ETL logic. With AWS Glue Custom Visual Transforms, data engineers can write and share business-specific Apache Spark logic, reducing dependence on Spark developers and making it simpler to keep ETL jobs up to date. These transforms are available to all jobs in your AWS account, whether visual or code-based.


Simplify Data Integration job development

AWS Glue Interactive Sessions, a serverless feature of job development, simplifies the development of data integration jobs. AWS Glue Interactive Sessions enables data engineers to interactively explore and prepare data. Engineers can explore, experiment on, and process data interactively using the IDE or notebook of their choice.

Built-in Job Notebooks

AWS Glue Studio Job Notebooks provides serverless notebooks with minimal setup in AWS Glue Studio, so developers can get started quickly. Glue Studio Job Notebooks provides a built-in interface for AWS Glue Interactive Sessions that lets users save and schedule their notebook code as AWS Glue jobs.

Build complex ETL pipelines with simple job scheduling

AWS Glue jobs can be invoked on a schedule, on-demand, or based on an event. You can start multiple jobs in parallel or specify dependencies across jobs to build complex ETL pipelines. AWS Glue will handle all inter-job dependencies, filter bad data, and retry jobs if they fail. All logs and notifications are pushed to Amazon CloudWatch so you can monitor and get alerts from a central service.

Apply and deploy DevOps best practices with Git integration

AWS Glue integrates with Git, the widely-used open source version control system. You can use GitHub and AWS CodeCommit to maintain a history of changes to your AWS Glue jobs and apply existing DevOps practices to deploy them. Git integration in AWS Glue works for all AWS Glue job types, whether visual or code-based. It includes built-in integration with both GitHub and AWS CodeCommit and also makes it simpler to use automation tools like Jenkins and AWS CodeDeploy to deploy AWS Glue jobs.

Reduce costs for non-urgent workloads with flexible job execution

AWS Glue Flex is a new flexible execution job class that allows you to reduce the cost of your non-urgent data integration workloads (e.g., pre-production jobs, testing, data loads, etc.) by up to 35%. Glue now has two job execution classes - standard and flexible. The standard execution class is ideal for time-sensitive workloads that require fast job startup and dedicated resources. Glue Flex is appropriate for non-time-sensitive jobs whose start and completion times may vary.

Read, insert, update, and delete files in your data lake

AWS Glue natively supports three open-source frameworks, including Apache Hudi, Apache Iceberg, and Linux Foundation Delta Lake. These frameworks help you manage data in a transactionally consistent manner for use in your Amazon Simple Storage Service (Amazon S3) based data lake.

Deliver high-quality data across your data lakes and pipelines (Preview)

AWS Glue Data Quality helps you improve your data quality and confidence. It automatically measures, monitors, and manages data quality in your data lakes and pipelines. It also automatically computes statistics, recommends quality rules, monitors, and alerts you when quality deteriorates, making it easier to identify missing, stale, or bad data before it impacts your business.


Visually transform data with a drag-and-drop interface

AWS Glue Studio allows you to author highly scalable ETL jobs for distributed processing without becoming an Apache Spark expert. Define your ETL process in the drag-and-drop job editor and AWS Glue automatically generates the code to extract, transform, and load your data. The code is generated in Scala or Python and written for Apache Spark.

Clean and transform streaming data in-flight

Serverless streaming ETL jobs in AWS Glue continuously consume data from streaming sources including Amazon Kinesis and Amazon MSK, clean and transform it in-flight, and make it available for analysis in seconds in your target data store. Use this feature to process event data like IoT event streams, clickstreams, and network logs. AWS Glue streaming ETL jobs can enrich and aggregate data, join batch and streaming sources, and run a variety of complex analytics and machine learning operations.

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